Key Takeaways
1. AI Ushers in a New Era of Economic Growth
The bottom line? It's going to be all right. In fact, better than all right, because AI is about to usher in a new industrial revolution that, for those who manage it properly, will generate significant economic growth.
Fourth Industrial Revolution. Artificial intelligence (AI) is driving a new industrial revolution, characterized by economic dislocation and the rise of "systems of intelligence." This era promises significant economic growth for those who can harness the power of AI, algorithms, bots, and big data. The rise of AI is poised to transform how we are educated, fed, transported, insured, medicated, and governed.
From Stall to Boom. The economy is transitioning from a "stall zone" to a period of technology-fueled growth. This "digital build-out" will see the fruits of digital technology move from Silicon Valley to the entire economy, creating opportunities for established companies to leverage their industry knowledge with the power of new machines. The key is to understand the new machine and situate it in the right business model.
The Promise of AI. AI is not just about entertainment or convenience; it's about addressing societal ills and improving the pillars of our society. Driverless cars promise to save lives, AI can help feed the world by optimizing the food supply chain, and medical misdiagnoses can plummet with AI-enhanced diagnostic processes. This is "digital that matters," and it will be driven by big brains both inside and outside of Silicon Valley.
2. The Digital Build-Out Requires Mastering the Three M's
They understand today's new raw materials (big data). They have built and now operate the new machines. And, most important, they have surrounded these new machines with business models that generate remarkable growth and profitability engines while expanding the overall market.
Aligning the Three M's. Success in the digital economy requires aligning three key elements: raw materials (data), new machines (systems of intelligence), and business models. Established companies are well-positioned for the digital build-out because they possess market knowledge, products, and assets to instrument. The Three M's have to be integrated and aligned to create value.
The Three M's Defined.
- Raw materials: Data generated from IoT devices and instrumentation of people, places, and things.
- New machines: Systems of intelligence that combine hardware, AI software, data, and human input.
- Business models: Commercial models that monetize services and solutions based on systems of intelligence.
GE's Transformation. General Electric (GE) serves as an example of a company reconfiguring itself around the Three M's. GE is instrumenting its products, building an IoT management platform (Predix), and selling insights based on the data, opening up new lines of business. GE now has a software business earning more than $6 billion in revenue.
3. AI Will Reshape, Not Eliminate, Most Jobs
Will many jobs be “automated away” in the coming years? Yes. However, for the vast majority of professions, the new machine will actually enhance and protect employment.
Job Automation, Enhancement, and Creation. The impact of AI on the labor force will be threefold: job automation (12% of existing jobs at risk), job enhancement (75% of existing jobs altered), and job creation (13% net new jobs). The focus should be on understanding what the new machine can and cannot do and how it will impact the future of work.
Manual vs. Knowledge Labor. Manual labor and knowledge labor are not interchangeable, and their substitution by machines is different. Knowledge automation is not zero-sum; it allows for more throughput and mass customization that was impossible before systems of intelligence. The automation of knowledge assets is not just a matter of removing existing labor.
Jobs vs. Tasks. Most analyses fail to distinguish between "jobs" and "tasks." Jobs are composed of various tasks, some of which can be automated while others will never be automated. The new machine will change jobs, but it will not eliminate them completely.
4. Systems of Intelligence: The Anatomy of the New Machine
A system of intelligence combines software (algorithms, business rules, machine-learning code, predictive analytics), hardware (servers, sensors, mobile devices, connectivity), data (contextualized and real-time), and human input (often judgment or questions).
Defining Systems of Intelligence. A system of intelligence combines software, hardware, data, and human input to create value. These systems are characterized by software that learns, massive hardware processing power, and huge amounts of data. The new machine is rapidly becoming the cornerstone for companies that compete on knowledge.
Key Attributes of AI:
- Software that learns: Machine-learning software improves over time, recognizing patterns and finding hidden insights without explicit programming.
- Massive hardware processing power: Cloud computing enables hyper-powerful computers to be tied together for blazing-fast performance.
- Huge amounts of data: Data is the fuel of the new economy, providing the context for personalized and curated experiences.
Narrow AI is Key. The focus should be on narrow AI (ANI), which is purpose-built and business-focused on a specific task. ANI is a tool that provides the basis for all that can be explored in the coming pages.
5. Data: The Fuel Powering the New Machine
Data is the fuel of the new economy.
Data as the New Oil. Data is the primary raw material of the Fourth Industrial Revolution. Like oil, data needs to be "mined," "refined," and "distributed." However, data is a potentially infinite resource that can grow quickly in scale and value.
Managing the Data Supply Chain. Turning data into actionable insight requires establishing and managing a "data supply chain" across the business. This involves exploration and extraction (upstream), refining (midstream), and distribution (downstream). The middle step, the refining of data or turning it into meaning, will be the key competitive battleground.
Instrument Everything. The strategic question shouldn't be "What should we instrument?" but rather "What shouldn't we instrument?" Instrumenting all operations, products, and customer experiences is essential for gaining proprietary insights and competitive advantage.
6. Digital Business Models: Five Ways to Win
This is where many people get stuck. They start tumbling down existential wormholes: Will machines need us? Who will control the machines? Will machines act in the best interests of humanity? Again, these are great questions that prompt fascinating discussions, all of which we like having as much as the next person, particularly with a glass of red wine on hand. But these discussions don't help you know what to do.
The AHEAD Model. The AHEAD model outlines five distinct approaches for winning with systems of intelligence: Automate, Halo, Enhance, Abundance, and Discovery. These are five specific approaches for winning with AI, each with its own set of approaches and tactics.
The Five Approaches:
- Automate: Outsource rote, computational work to the new machine.
- Halo: Instrument products and people and leverage the data exhaust to create new customer experiences and business models.
- Enhance: View the computer as a colleague that can increase job productivity and satisfaction.
- Abundance: Use the new machine to open up vast new markets by dropping the price point of existing offers.
- Discovery: Leverage AI to conceive entirely new products, new services, and entirely new industries.
Hybrid Models. The winning business models will be hybrid—part physical, part digital. The challenge is to decide what goes where, what stays physical, and what goes digital.
7. Automation: The Foundation for Digital Transformation
Software Should Be Eating Your Core Operations.
Automation as a Means, Not an End. Automation is the initial step in each industrial revolution. It is a necessary "evil" for delivering at the "Google price." However, the next wave of automation will pave the way for invention and economic expansion through the four subsequent plays.
Targeting Core Operations. The best areas for automation are in the back and middle offices, including information technology, finance, human resources, claims processing, and customer service. These areas comprise an information supply chain that gathers, synthesizes, transforms, and distributes data.
Rules of the Road for Automation:
- Set a 25%-25% automation imperative (25% cost reduction and 25% productivity increase).
- Find process-automation targets with repetitive tasks, low demand for human judgment, and high volumes of data.
- Break through the "brass wall" by addressing the concerns of middle managers.
- Build a repeatable process to obliterate work.
8. Halo: Instrument Everything to Create New Value
Every “Thing” Is Now a Code Generator.
The Power of Code Halos. Instrumenting products and people and leveraging the data exhaust they generate (Code Halos) can create new customer experiences and business models. The race is on to win through instrumentation, and established companies are changing the rules of competition.
Three New Rules of Competition:
- Instrumentation is no longer elective; it is now core curriculum.
- Code is more valuable than things.
- Never turn them off.
Discovery Health's Success. Discovery Health in South Africa uses its Vitality platform to provide economic incentives to its members based on whether they participate in healthy behaviors. This demonstrates how instrumenting products and people can create new value propositions and customer intimacy.
9. Enhance: Amplify Human Performance with AI
You + New Tools = Enhancement.
The Power of Enhancement. The new machine can increase job productivity and satisfaction. Entire vocations, from sales to nursing to teaching, will be revolutionized with the power of computer-based enhancement. Workers will come to view the new machine as their trusted colleague.
Enhanced Jobs Are Protected Jobs. The vast majority of white-collar work will be made better with the new machines. The key is to identify the roles, processes, systems, and experiences that can be upgraded by newly available technologies.
Smart Robots Make Smarter Hands. The combination of "smart hands" and "smart robots" is becoming more visible all the time. The machine continually gets smarter, and so does the human.
10. Abundance: Unlock New Markets by Lowering Prices
Increasing Prosperity by Lowering Prices.
The Concept of Abundance. As prices go down, demand goes up. The new machine can be used to open up vast new markets by dropping the price point of existing offers.
Narayana Health's Example. Narayana Health (NH) in India is revolutionizing cardiac surgery by leveraging digital technology. By lowering the cost of heart surgery approximately a hundred-fold, NH now provides world-class heart surgeries to anyone in need.
Key Strategies for Abundance:
- Obsess about the start-up community.
- Kill your company (ask employees to identify ways to disrupt your existing business).
- Play the "tomorrow it's free" game.
- Manage your innovator's dilemma.
- Make like a maker.
- Think like a corner shop (new personalization).
- Apply digital Taylorism.
11. Discovery: Manage Innovation for the Digital Economy
Discovery Is Hard, but Not as Hard as Being Irrelevant.
The Importance of Discovery. Discovery, or blue-sky innovation, is both a catalyst for and an outcome of the AHEAD model. It is central to remaining relevant in the great digital build-out.
R&D Without AI Is No R&D At All. The new machine will be the platform of innovation. Once you are instrumenting, automating, tracking, and analyzing the core operations of your business and applying machine learning, innovation opportunities will be consistently unearthed.
Key Strategies for Discovery:
- Apply digital Kaizen (continuous, incremental improvements).
- Let hits pay for misses (manage a portfolio of initiatives).
- Leave the past behind (decommission legacy systems).
- Play the Wayback Game (understand how quickly technology changes).
12. Competing on Code: A Call to Action
Courage and Faith in the Future.
The Future Is Here. The digital build-out is here, and it's time to act. The new machines will continue to amaze, will be embedded most everywhere and in most everything, and will increasingly do more and more of the work people do today.
Align the Three M's. To win in the digital economy, you must align the Three M's: raw materials (data), new machines (systems of intelligence), and business models.
Move AHEAD. Embrace the AHEAD model to demystify the application of the new machine in your business: Automate, Halo, Enhance, Abundance, and Discovery.
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FAQ
1. What is What To Do When Machines Do Everything by Malcolm Frank about?
- AI’s Transformative Impact: The book explores how artificial intelligence, algorithms, bots, and big data are fundamentally changing work, business, and society.
- Practical Roadmap: It introduces a structured approach (the AHEAD model) to help individuals and organizations adapt to and thrive in the AI era.
- Optimistic Perspective: The authors argue that, rather than just causing job loss, AI will drive a new industrial revolution, creating economic growth and new opportunities.
- Focus on Action: The book emphasizes separating hype from reality and provides actionable guidance for navigating the digital transformation.
2. Why should I read What To Do When Machines Do Everything by Malcolm Frank?
- Actionable Strategies: The book offers concrete, pragmatic advice for business leaders and workers facing immediate challenges from AI and automation.
- Comprehensive Framework: It presents the AHEAD model, a clear and actionable framework for understanding and implementing AI-driven change.
- Historical and Real-World Context: The authors ground their analysis in historical patterns and provide real-world case studies from companies like GE and Netflix.
- Balanced and Optimistic: The book balances optimism about AI’s potential with practical advice on managing change and overcoming resistance.
3. What are the key takeaways and main concepts from What To Do When Machines Do Everything by Malcolm Frank?
- AHEAD Model: The book’s central framework—Automate, Halo, Enhance, Abundance, Discovery—guides organizations through digital transformation.
- Systems of Intelligence: It highlights the rise of integrated AI and data-driven platforms that automate processes, generate valuable data, and enhance human performance.
- Data as a Strategic Asset: Data (“Code Halos”) is positioned as the new raw material, more valuable than physical assets in the digital economy.
- Call to Action: Leaders are urged to act decisively, automate, instrument everything, enhance human capabilities, drive down prices, and foster continuous innovation.
4. How does What To Do When Machines Do Everything by Malcolm Frank define artificial intelligence (AI)?
- Practical Definition: AI is defined as an area of computer science focused on machines that learn, with an emphasis on practical, narrow applications.
- Narrow vs. General AI: The book distinguishes between narrow AI (task-specific, business-focused) and general/super AI (human-level or beyond), noting that current AI is narrow.
- Business Value Focus: The authors stress that AI’s value lies in what machines do well, not in replicating human intelligence.
- Avoiding Hype: The book cautions against getting distracted by speculative fears about superintelligent AI.
5. What is the AHEAD model in What To Do When Machines Do Everything by Malcolm Frank, and how does it work?
- Automate: Outsource repetitive, rule-based tasks to AI and bots to improve efficiency and reduce costs.
- Halo (Code Halos): Instrument people, products, and processes to generate data “halos” that enable new customer experiences and business models.
- Enhance: Use AI to amplify human performance, making jobs more productive and satisfying.
- Abundance: Leverage AI to lower prices and democratize access, creating new markets and opportunities.
- Discovery: Employ AI to drive innovation, inventing new products, services, and industries.
6. What are “systems of intelligence” according to What To Do When Machines Do Everything by Malcolm Frank?
- Integrated Platforms: Systems of intelligence combine software (algorithms, machine learning), hardware (servers, sensors), data, and human input to create business value.
- Continuous Learning: These systems feature software that learns and improves over time, fueled by massive data and processing power.
- Business Disruption: They underpin companies like Uber, Netflix, and Facebook, enabling real-time, personalized, and scalable services.
- Redefining Competition: Systems of intelligence disrupt traditional industries and redefine how companies compete.
7. How does What To Do When Machines Do Everything by Malcolm Frank describe the impact of AI on jobs and the future of work?
- Job Automation and Enhancement: The book estimates about 12% of jobs may be automated away, 75% will be enhanced, and 13% new jobs will be created.
- Task-Based View: Jobs are made up of tasks; some will be automated, others enhanced, so entire jobs rarely disappear overnight.
- Historical Patterns: Automation has historically led to new abundance and employment, despite initial job losses.
- Churn and Opportunity: The authors predict significant job churn but overall opportunity and job creation if AI is managed wisely.
8. How does What To Do When Machines Do Everything by Malcolm Frank explain automation in the AI era?
- Targeting Automation: Focus on automating high-volume, repetitive, rule-based tasks with low need for human judgment or empathy.
- Overcoming Resistance: The “brass wall” of middle management often resists automation; leaders must engage skeptics and manage change proactively.
- Structured Approach: Set bold goals, start small, prototype, pilot, analyze, and repeat to build sustainable automation processes.
- Freeing Human Potential: Automation allows humans to focus on complex, nuanced, and creative work.
9. What are Code Halos in What To Do When Machines Do Everything by Malcolm Frank, and why are they important?
- Definition: Code Halos are digital “halos” of data generated by instrumenting people, products, and processes, creating a virtual twin of physical entities.
- Strategic Value: Mastering Code Halos enables highly personalized customer experiences, improved products, and new revenue streams.
- Competitive Rules: Instrumentation is now core, data is often more valuable than physical things, and always-on connections maximize value.
- Risk of Ignoring: Failing to leverage Code Halos is considered corporate malpractice in the digital economy.
10. How does What To Do When Machines Do Everything by Malcolm Frank describe the enhancement of human work with AI?
- Enhancement Over Replacement: AI will mostly enhance, not replace, human jobs, especially in knowledge work like teaching, nursing, and law.
- Human-Machine Synergy: AI tools act as “white-collar exoskeletons,” making workers more productive and creative.
- Doubling Down on Humanity: As machines take over routine work, humans can focus on empathy, relationships, and complex judgment.
- Competitive Advantage: Companies that invest in human-centric experiences, enabled by automation, gain a competitive edge.
11. What does What To Do When Machines Do Everything by Malcolm Frank say about creating abundance with AI and automation?
- Abundance Defined: Automation and digitization dramatically lower prices, increasing demand and democratizing access to products and services.
- Continuous Improvement: Digital products benefit from exponential improvements in cost and performance, requiring ongoing innovation.
- Strategic Approaches: The book recommends learning from startups, planning for “tomorrow it’s free” scenarios, and personalizing offerings.
- Unlocking Growth: Embracing abundance helps companies find new growth opportunities and expand markets.
12. How does What To Do When Machines Do Everything by Malcolm Frank recommend managing innovation and discovery in the digital economy?
- Discovery as Core Function: Innovation is central to survival and growth, not a side project.
- AI-Driven R&D: Use AI platforms to analyze data and uncover new product, process, and business model opportunities.
- Portfolio Approach: Balance incremental improvements (“digital Kaizen”) with bold moonshot projects, accepting failure as part of the process.
- Overcoming Inertia: Leaders must overcome legacy IT and organizational resistance to enable continuous innovation and discovery.
Review Summary
What To Do When Machines Do Everything receives mixed reviews. Some praise its insights on AI's impact on business and provide practical advice for adapting to technological changes. Others criticize it for being overly optimistic, lacking depth, and primarily targeting high-level executives. Reviewers appreciate the book's balanced approach to AI's potential benefits and challenges, but some find it repetitive and lacking in novel ideas. The book's framework for automation and digital transformation is highlighted as useful, though some readers desire more concrete, actionable steps for implementation.
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